Current speech-to-text conversion operations, such as would be performed by generally available voice recognition software, provide only a continuous text block in which individual words may be editable. However, it will not be apparent to the user why an error may have been made because it is unclear in what context individual words were understood by the conversion engine (e.g., interpreted as a street name when a restaurant name was intended). It therefore also remains unclear how best to correct individual errors, or for the user to adapt the manner in which they utilize the voice recognition software in the future.
Accordingly, there is a need for providing context-based corrections of voice recognition results such that the user gains a better understanding as to why certain speech input may have been incorrectly interpreted, and thereby enable the user to adapt future speech input accordingly. Moreover, it is further desirable to enable the user to correct the text-based results in a manner which indicates the context in which the error occurred, thereby facilitating text editing operations.